85 West St,

New York,

NY,

USA,

ZIP: 10006

Phone: +4917652567124

The Industrial Conference on Data Mining ICDM is held on yearly basis.

Researcher and Industrial People from different fields will present theoretical aspects and their applications, and the results obtained by applying data mining. Besides that, newcomers in the field can get a fast introduction to Data Mining by taking the tutorial running in connection with the conference.

In a special industry session practicioners from different industrial branches can present their ongoing projects and discuss their work with the auditorium. An industrial exhibition, where companies present their data-mining tools, will round up the conference.

Four workshops are running in connection with the conference: Data Mining in Life Sciences DMLS, Case-Based Reasoning CBR-MD, Data Mining in Marketing, and Workshop Data Mining in Agriculture. Other proposals for workshops are welcome and can be submitted to [email protected].

In a problem/solution hour you will have the opportunity to present your application and ask for support by researchers.

The social events will give you the opportunity to meet top leading researchers in Data Mining and Machine Learning from all over the world.

Target groups of ICDM
Researchers
Researchers doing theoretical and applied research in data mining.

Practicioners
Practicioners from different industrial, social or economic branches such as for example:

Marketing
Database marketing companys, direct marketing companys, credit scoring service providers, Marketing service providers, mailorderhouses, insurance companys, financial service providers marketing agenturen, e-commerce companys

Managers and specialists for analytical CRM, database marketing, sales, operations research, produktmanagement and IT

Customer-Relationship Managment
Managers and specialists for analytical CRM, database marketing, sales, operations research, produktmanagement and IT

Industry Automation Engineers dealing with the automation of industrial processes, where a lot of data have to be summarized to give new guidelines for process control, product development, and quality assurance

Medicine
Physicians and veterinarians, who have to convert subjective data (i.e. the colouring in histology and cytology) into objective clinical results.

System Biology, Genetics
System Biology, Geneticists, who, in the scope of Genomics and Proteomics, have to detect the minority of disease-relevant data out of millions of poorly understood basis data.

Public Sector, Finance
Financal Manager, economists and lawyers engaged in politics and management, who have to develop new guidelines and payment directives in the framework of the health reform law and are to develop means of decision making, which are logically deducible from data bases, documents and pragmatic approaches.
The Industrial conferences on Data Mining ICDM is held on yearly basis. Experts from different fields will present their applications and the results obtained by applying data mining. Besides that, newcomers in the field can get a fast introduction to Data Mining by taking the tutorial running in connection with the conference. In a problem/solution hour you will have the opportunity to present your application and ask for support by others or for cooperation in solving the problem.

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Topics of the conference
Paper submissions should be related but not limited to any of the following topics:

Applications of Data Mining in ...

Marketing
Medicine
Civil Engineering
E-Commerce (Mining Logfiles)
Biotechnology
Quality Management
Multimedia Data (Image, Video, Text, Signals)
Web-Mining
Intrusion Detection in Networks
Criminology
Telecommunications
Social Sciences
Forensic Data Analysis
Drug Discovery
Agriculture
Smart Maintenance
Legal Court Cases
Energy Industries
Logistics and Supply Chain Management
Finance and Stock Markets
Meterology and more ...

Theoretical and Application-oriented Topics in ...

Big Data and Algorithm for Big Data
Case-Based Reasoning and Similarity-Based Reasoning
Clustering
Classification and Prediction
Statistical Learning
Association Rules
Deviation and Novelty Detection
Control Charts
Conceptional Learning
Goodness Measures and Evaluation (e.g. false discovery rates)
Inductive Learning Including Decision Tree and Rule Induction Learning
Organisational Learning and Evolutional Learning
Sampling Methods
Similarity Measures and Learning of Similarity
Statistical Learning and Neural Net Based Learning
Visualization and Data Mining
Deviation and Novelty Detection
Feature Grouping, Discretization, Selection and Transformation
Feature Learning
Frequent Pattern Mining
Learning and Adaptive Control
Learning/Adaption of Recognition and Perception
Learning for Handwriting Recognition
Learning in Image Pre-Processing and Segmentation
Mining Financial or Stockmarket Data
Mining Motion from Sequence
Subspace Methods
Support Vector Machines
Time Series and Sequential Pattern Mining
Desirabilities
Graph Miningbigdata
Agent Data Mining
Applicatio

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Tickets: https://go.evvnt.com/813231-1?pid=4528

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